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1. Tissue Forge: Interactive biological and biophysics simulation environment

   https://doi.org/10.1371/journal.pcbi.1010768  

   Sego, T. J.; Sluka, James P.; Sauro, Herbert M.; Glazier, James A. (October 2023, PLOS Computational Biology)

   Kemp, Melissa L. (Ed.)

   Tissue Forge is an open-source interactive environment for particle-based physics, chemistry and biology modeling and simulation. Tissue Forge allows users to create, simulate and explore models and virtual experiments based on soft condensed matter physics at multiple scales, from the molecular to the multicellular, using a simple, consistent interface. While Tissue Forge is designed to simplify solving problems in complex subcellular, cellular and tissue biophysics, it supports applications ranging from classic molecular dynamics to agent-based multicellular systems with dynamic populations. Tissue Forge users can build and interact with models and simulations in real-time and change simulation details during execution, or execute simulations off-screen and/or remotely in high-performance computing environments. Tissue Forge provides a growing library of built-in model components along with support for user-specified models during the development and application of custom, agent-based models. Tissue Forge includes an extensive Python API for model and simulation specification via Python scripts, an IPython console and a Jupyter Notebook, as well as C and C++ APIs for integrated applications with other software tools. Tissue Forge supports installations on 64-bit Windows, Linux and MacOS systems and is available for local installation via conda. 

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2. ABIDES: Towards High-Fidelity Multi-Agent Market Simulation

   https://doi.org/10.1145/3384441.3395986  

   Byrd, David; Hybinette, Maria; Balch, Tucker Hybinette (June 2020, SIGSIM-PADS '20: Proceedings of the 2020 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation)

   We introduce ABIDES, an open source Agent-Based Interactive Discrete Event Simulation environment. ABIDES is designed from the ground up to support agent-based research in market applications. While proprietary simulations are available within trading firms, there are no broadly available high-fidelity market simulation environments. ABIDES enables the simulation of tens of thousands of trading agents interacting with an exchange agent to facilitate transactions. It supports configurable pairwise noisy network latency between each individual agent as well as the exchange. Our simulator's message-based design is modeled after NASDAQ's published equity trading protocols ITCH and OUCH. We introduce the design of the simulator and illustrate its use and configuration with sample code, validating the environment with example trading scenarios. The utility of ABIDES for financial research is illustrated through experiments to develop a market impact model. The core of ABIDES is a general-purpose discrete event simulation, and we demonstrate its breadth of application with a non-finance work-in-progress simulating secure multiparty federated learning. We close with discussion of additional experimental problems it can be, or is being, used to explore, such as the development of machine learning trading algorithms. We hope that the availability of such a platform will facilitate research in this important area. 

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3. HLPerf: Demystifying the Performance of HLS-based Graph Neural Networks with Dataflow Architectures

   https://doi.org/10.1145/3655627  

   Zhao, Chenfeng; Faber, Clayton J.; Chamberlain, Roger D.; Zhang, Xuan (April 2024, ACM Transactions on Reconfigurable Technology and Systems)

   The development of FPGA-based applications using HLS is fraught with performance pitfalls and large design space exploration times. These issues are exacerbated when the application is complicated and its performance is dependent on the input data set, as is often the case with graph neural network approaches to machine learning. Here, we introduce HLPerf, an open-source, simulation-based performance evaluation framework for dataflow architectures that both supports early exploration of the design space and shortens the performance evaluation cycle. We apply the methodology to GNNHLS, an HLS-based graph neural network benchmark containing 6 commonly used graph neural network models and 4 datasets with distinct topologies and scales. The results show that HLPerf achieves over 10 000 × average simulation acceleration relative to RTL simulation and over 400 × acceleration relative to state-of-the-art cycle-accurate tools at the cost of 7% mean error rate relative to actual FPGA implementation performance. This acceleration positions HLPerf as a viable component in the design cycle. 

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4. A Web-based System for Contagion Simulations on Networked Populations

   Ferdousi, T.; Kishore, A.; Machi, L.; Machi, D.; Kuhlman, C.; Ravi, S.S. (October 2022, IEEE eScience)

   Motivated by a wide range of applications, research on agent-based models of contagion propagation over networks has attracted a lot of attention in the literature. Many of the available software systems for simulating such agent-based models require users to download software, build the executable and set up execution environments. Further, running the resulting executable may require access to high performance computing clusters. Our work describes an open access software system (NetSimS) that works under the “Modeling and Simulation as a Service” (MSaaS) paradigm. It allows users to run simulations by selecting agent-based models and parameters, initial conditions, and networks through a web interface. The system supports a variety of models and networks with millions of nodes and edges. In addition to the simulator, the system includes components that allow users to choose initial conditions for simulations in a variety of ways, to analyze the data generated through simulations, and to produce plots from the data. We describe the components of NetSimS and carry out a performance evaluation of the system. We also discuss two case studies carried out on large networks using the system. NetSimS is a major component within net.science, a cyberinfrastructure for network science. Index Terms—Agent-Based Simulation, Contagion, Networks, Modeling and Simulation as a Service, Cyberinfrastructure 

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5. COMBS: First Open-Source Based Benchmark Suite for Multi-physics Simulation Relevant HPC Research

   Dowling A., Swiatowicz F. (September 2020, Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science)

   null (Ed.)

   Recent scientific computing increasingly relies on multi-scale multi-physics simulations to enhance predictive capabilities by replacing a suite of stand-alone simulation codes that independently simulate various physical phenomena. Inevitably, multi-physics simulation demands high performance computing (HPC) through advanced hardware and software accelerating due to its intensive computing workload and run-time communication needs. Thus, its research has become a hotspot across different disciplines. However, it is observed that most benchmarks used in the evaluation of corresponding work are through commercial or in-house codes. Then, the lack of accessible open-source multi-physics benchmark suites has presented a challenge in uniformly evaluating simulation performance across related disciplines. This work proposes the first open-source based benchmark suite with 12 selected benchmarks for research in multi-physics simulation, the Clarkson Open-Source Multi-physics Benchmark Suite (COMBS). Multiple metrics have been gathered for these benchmarks, such as instructions per second and memory usage. Also provided are build and benchmark scripts to improve usability. Additionally, their source codes and installation guides are available for downloading through a github repository built by the authors. The selected benchmarks are from key applications of multi-physics simulation and highly cited publications. It is believed that this benchmark suite will facilitate to harness the full potential of HPC research in the field of multi-physics simulation. 

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